AI/CD: What Happens When AI Augments Your Development Pipeline
February 24, 2026
The traditional software development cycle looks like this:
- Developer writes code
- Developer commits code
- CI/CD runs tests
- If tests pass, code deploys
- Repeat
It's better than manually deploying code, but the human is still the bottleneck. The developer has to write the code. The developer has to decide what to build. The developer has to fix the bugs.
What if the AI did most of that?
From CI/CD to AI/CD
CI/CD stands for Continuous Integration / Continuous Delivery. It's the infrastructure that automates testing and deployment.
AI/CD — Agentic Integration / Continuous Delivery — goes further. It automates the development itself.
| Model | Full Name | What It Automates |
|---|---|---|
| CI/CD | Continuous Integration / Continuous Delivery | Build, test, deploy — humans write the code |
| AI/CD | Agentic Integration / Continuous Delivery | Implementation, tests, reviews — humans approve |
| AI/AD | Agentic Integration / Autonomous Delivery | Full lifecycle under policy — humans set strategy |
Most teams today are on CI/CD. AI/CD is the next step — and it's where the leverage is for small teams right now.
Here's the AI/CD loop:
- You describe what you want
- AI writes the code
- AI tests the code
- AI fixes failures
- You approve
- Code deploys
The human is still in the loop. But instead of writing code, you're reviewing and approving code.
What "Agentic" Actually Means
"Agentic AI" is a term that gets thrown around a lot. Here's what it means in practice:
The AI doesn't just respond to prompts. It takes action, evaluates results, and iterates.
Non-agentic: "Write a function that does X." Agentic: "Build feature X. Test it. If tests fail, fix it. When it works, prepare it for deployment."
The AI is an agent — it acts on your behalf, within boundaries you set.
In an AI/CD pipeline, the AI:
- Reads the codebase
- Understands the context
- Writes new code that fits the existing patterns
- Runs tests
- Interprets failures
- Tries again
- Repeats until it works or escalates
The Human-in-the-Loop
Let me be clear: this isn't "AI replaces developers." This is "AI handles the routine so developers focus on the hard stuff."
The human-in-the-loop model means:
- AI proposes changes
- Human reviews and approves (or rejects)
- Approved changes deploy
You're not blindly trusting AI to push to production. You're reviewing its work before it goes live.
Think of it as augmenting your team with an AI collaborator that:
- Operates continuously across your codebase
- Maintains consistent output regardless of workload
- Can explore multiple solution approaches in parallel
- Improves through feedback over time
You still provide direction. You still review the work. But the bottleneck shifts from "waiting for someone to write code" to "reviewing and approving code."
What AI/CD Is Good At
AI/CD shines for certain types of work:
Routine features: CRUD operations, API endpoints, standard UI components. The AI has seen thousands of examples of these patterns.
Bug fixes: Given a failing test and stack trace, AI can often identify and fix the issue faster than a human.
Refactoring: Updating syntax, migrating to new patterns, cleaning up technical debt.
Test coverage: Writing unit tests, integration tests, edge case tests.
Documentation: Generating docs from code, keeping docs in sync.
What AI/CD Struggles With
AI/CD isn't magic. It has real limitations:
Novel architecture: AI can implement patterns it's seen before. It struggles with genuinely new approaches.
Complex business logic: The more context required, the more likely the AI gets it wrong.
Security-critical code: AI can introduce subtle vulnerabilities. Critical paths need human review.
Cross-cutting concerns: Changes that touch many parts of the system are riskier.
The 80/20 rule applies: AI can handle 80% of the routine work. The 20% that requires judgment, creativity, or deep context still needs humans.
The Practical Setup
An AI/CD pipeline requires:
1. Code access: The AI needs to read and write to your repositories (GitHub, GitLab, etc.)
2. Context understanding: The AI needs to understand your codebase structure, patterns, and conventions.
3. Test infrastructure: Automated tests that verify correctness. The AI uses tests as feedback.
4. Approval workflow: A way for humans to review and approve before deployment.
5. Guardrails: Rules about what the AI can and can't touch. Rate limits. Security boundaries.
Here's what the full lifecycle looks like:
| Step | What Happens | Who Does It |
|---|---|---|
| 1. Intake | Define what needs to be built | Human |
| 2. Spec | Align requirements, tests, contracts | Human + AI |
| 3. Implementation | Write the code | AI |
| 4. Testing | Run tests, fix failures, iterate | AI |
| 5. Review | Approve or reject the changes | Human |
| 6. Deploy | Merge and release | Automated |
The key difference from traditional CI/CD: steps 3 and 4 are handled by AI. Humans stay in control at every gate — you're reviewing and approving, not writing boilerplate.
Setup takes 4-6 weeks. After that, the pipeline runs continuously.
The Impact on Small Teams
For startups and small teams, AI/CD changes what's possible without changing your headcount.
A 3-person team with AI/CD can ship like a 6-person team. Not because AI is smarter than your engineers — because it handles the routine work (CRUD, boilerplate, tests, docs) while your humans focus on the hard stuff.
The features sitting in your backlog because "we don't have bandwidth"? They can start moving.
The technical debt you keep deprioritizing? AI can chip away at it in the background.
The test coverage you know you need but never have time for? AI can write tests for existing code.
It doesn't replace your team. It gives them leverage.
What This Costs
AI/CD isn't free. You're paying for:
- AI compute (tokens for each code generation, test run, iteration)
- Infrastructure (the pipeline itself)
- Human review time (someone still approves)
For a typical startup, expect:
- $7,500 setup
- $2,500/month (AI credits, hosting, maintenance)
- 2-4 hours/week of human review time
That's still cheaper than a junior developer — and it works nights and weekends.
The ROI depends on how much development work you need done. If you're blocked on dev capacity, AI/CD can unlock significant value. If you're not building actively, it's not worth it.
Is This the Future?
I think AI/CD is where software development is heading — not because AI will replace developers, but because the cost of building software is dropping.
When generating code costs near zero, the scarce resource shifts from "writing code" to "deciding what to build" and "reviewing what was built."
That's a different skill set. And it's a different kind of bottleneck.
At Rewired Consulting, we set up AI/CD pipelines for startups and small teams who want to ship more without hiring more. Learn more about AI/CD.